Maximising the accuracy of image-based surface sediment sampling techniques

Recent years have seen increased interest in automated methods, utilizing photographs collected with a hand-held digital camera, for determining the grain-size distribution of coarse river sediments. Such methods are as precise as traditional field methods, and have considerable time and cost advantages. Nevertheless, several unresolved issues pertaining to their deployment remain to be addressed. Using datasets collected from seven gravel-bed rivers, this paper examines four key issues: (i) the minimum area required to obtain a representative sample; (ii) the effect of lower-end truncation on grain-size percentiles; (iii) the effect of river-bed structure such as imbrication and hiding; and (iv) the potential benefits of using individual particle measurements rather than the number (or mass) of particles per size class to calculate percentiles. It is demonstrated that sampling areas of between 50 and 200- times that of the largest grain are adequate to achieve percentile errors of <10% (in mm). The appropriateness of lower-end truncation depends on the study aims and sediment properties. It has a limited effect on higher percentiles, except where sand is a major constituent. Understanding the influence of bed structure on image-derived size information is complicated by the absence of error-free benchmarks against which accuracy may be evaluated, but it is likely that other errors are more important. The use of individual particle measurements to calculate percentiles in preference to classified data is shown to have a small, but appreciable, effect on precision. These results will assist practitioners in making appropriate operational decisions to maximize data quality using image-based grain-size data capture

The Ant and the Grasshopper: contrasting responses and behaviors to water stress of riparian trees along a hydroclimatic gradient

Riparian trees are particularly vulnerable to drought because they are highly dependent on water availability for their survival. However, the response of riparian tree species to water stress varies depending on regional hydroclimatic conditions, making them unevenly vulnerable to changing drought patterns. Understanding this spatial variability in stress responses requires a comprehensive assessment of water stress across broader spatial and temporal scales. Yet, the precise ecophysiological mechanisms underlying these responses remain poorly linked to remotely sensed indices. To address this gap, the implementation of remote sensing methods coupled with in situ validation is essential to obtain consistent results across diverse spatial and temporal contexts. We conducted a multi-tool analysis combining multispectral and thermal remote sensing indices with in situ ecophysiological measurements at different temporal scales to analyze the responses of white poplar (Populus alba) to seasonal changes in drought along a hydroclimatic gradient

The natural wood regime in rivers

International audienc

Bedload infilling and depositional patterns in chute cutoffs channels of a gravel‐bed river: The Ain River, France

International audienc

A high-resolution temporal framework to understand the reach-scale controls on wood budgeting

International audienceLarge active channels usually store more wood than channels with a narrow flow because of the availability of large unvegetated bars for wood deposition and inner functioning that usually supplies more wood through channel shifting. However, the dynamics of the wood supply (wood input, output, or stability) can vary substantially over time and the drivers are largely unknown. To explore them, we studied the temporal variability of large wood pieces and logjams along a 12-km reach of the lower Allier River using six series of aerial images of variable resolution acquired between 2009 and 2020, during which maximum river discharge fluctuated around the biannual (Q2) flood magnitude. We show that the wood budget was controlled by specific hydrological conditions. Wood output was best explained by water levels exceeding bankfull discharge (Q1.5). The duration of the highest magnitude flood (over bankfull discharge) was the best predictor of wood inputs, with shorter floods resulting in higher input rates. Finally, most of the wood remained stable when the river discharge did not exceed 60% of the bankfull discharge over a long period of time. Hydrological conditions driving jam build-up and removal were similar to those controlling individual wood piece dynamics. A succession of floods of similar (relatively low ~ Q2) magnitude and decreasing flood duration since 2016 have probably reinforced the filtering effect of wood obstacles, leading to positive feedback, which has been strengthened by riparian vegetation colonisation of the active channel